Haris Hamzah, author
Prediksi hubungan struktur molekul dan aktivitas biologi inhibitor dipeptidyl peptidase-4 menggunakan metode deep neural network dengan metode pemilihan fitur catboost = Prediction of molecular structure and biological activity relationship of dipeptidyl peptidase-4 inhibitors using deep neural networks with catboost as feature selection method
Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2020
 UI - Tesis (Membership)
Haris Hamzah, author
Prediksi hubungan struktur molekul dan aktivitas biologi inhibitor dipeptidil peptidase-4 menggunakan metode deep neural network dengan metode pemilihan fitur catboost = Prediction of molecular structure and biological activity relationship of dipeptidyl peptidase-4 inhibitors using deep neural networks with catboost as feature selection method
Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2020
 UI - Tesis (Membership)
Adawiyah Ulfa, author
Evaluasi Klasifikasi Hubungan Kuantitatif Struktur Aktivitas Molekul dengan Model Hybrid Deep Learning dan Pemilihan Fitur Recursive Feature Elimination pada Inhibitor Dipeptidyl Peptidase-4 = Evaluation of the Classification in Quantitative Structures Activity Relationships of Molecular with Hybrid Deep Learning Models and Selection Features of Recursive Feature Elimination in Dipeptidyl Peptidase-4 Inhibitors
Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2021
 UI - Tesis (Membership)
Sarah Syarofina, author
Analisis Pemilihan Molekul Inhibitor Dipeptidil Peptidase 4 pada Perancangan Obat Diabetes Tipe 2 menggunakan Algoritma K-Modes Clustering dengan Levenshtein Distance = Molecular Selection Analysis of Dipeptidyl Peptidase-4 Inhibitors in The Drug Discovery of Type 2 Diabetes using K-Modes Clustering Algorithm with Levenshtein Distance
Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2020
 UI - Tesis (Membership)
Revania Rismarini, author
Klasifikasi Data Soft Tissue Tumor menggunakan Deep Neural Network dengan Seleksi Fitur Signal-to-Noise Ratio = Classification of Soft Tissue Tumor using Deep Neural Network with Signal-to-Noise Ratio Feature Selection
Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Indonesia, 2019
 UI - Skripsi (Membership)
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